Uso de Técnicas de Análise de Sentimentos em Tweets relacionados ao Meio-Ambiente
Abstract
The proliferation of social media on the Web and the need for companies to measure the impact of their environmental initiatives by the affected population has driven the proposal of an automatic mechanism to analyze the population's opinions. This necessity becomes even more evident especially regarding companies that deal directly with high environmental risk. In this context, the goal of this work is to apply sentiment analysis techniques on tweets related to environmental issues aiming to help energy companies analyze the impacts of environmental actions taken over time through opinions within social media.
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Eirinaki, M., Pisal, S., and Singh, J. Feature-based opinion mining and ranking. Journal of Computer and System Sciences 78, 4 (July 2012), 1175–1184.
Fang, Y., Si, L., Somasundaram, N., Yu, Z.: Mining contrastive opinions on political texts using cross-perspective topic model. In: Proceedings of the fifth ACM international conference on Web search and data mining - WSDM’12. p. 63. ACM Press, New York, USA (2012).
Feldman, R.: Techniques and applications for sentiment analysis. Communications of the ACM 56(4), 82 (Apr 2013).
Hu, M., and Liu, B. Mining and summarizing customer reviews. In KDD ’04 (New York, New York, USA, 2004), ACM, ACM Press, p. 168.
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on WWW ’10. p. 591. ACM Press, New York (2010).
Li, Y.M., Li, T.Y.: Deriving Marketing Intelligence over Microblogs. In: 2011 44th
Hawaii International Conference on System Sciences. pp. 1–10. IEEE (Jan 2011) Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (May 2012).
Nascimento, P., Aguas, R., Lima, D.D., Kong, X., Osiek, B.: Análise de sentimento de tweets com foco em notícias. In I Brazilian Workshop on Social Network Analysis and Mining, 2012.
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O’Hare, N., Davy, M., Bermingham, A., Ferguson, P., Sheridan, P., Gurrin, C., and Smeaton, A. F. Topic-dependent sentiment analysis of financial blogs. In Proceeding of the TSA ’09 (New York, USA, 2009), ACM Press, p. 9.
Pak, A., Paroubek, P.: Twitter as a Corpus for Sentiment Analysis and Opinion Mining. Proceedings of the Seventh conference on International Language Resources and Evaluation LREC’10 (ELRA), 1320–1326 (2010).
Pang, B., and Lee, L. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2, 2 (2008), 1–135.
Read, J.: Using Emoticons to reduce Dependency in Machine Learning Techniques for Sentiment Classification (June), 43–48 (2005).
Sarmento, L., Carvalho, P., Silva, M.J., de Oliveira, E.: Automatic creation of a reference corpus for political opinion mining in user-generated content. In: Proceeding of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion - TSA ’09. p. 29. ACM Press, New York (2009).
Sharma, A., and Dey, S. A comparative study of feature selection and machine learning techniques for sentiment analysis. In RACS ’12 (New York, USA), ACM Press, p. 1.
Tumitan, D., Becker, K.: Tracking Sentiment Evolution on User-Generated Content : A Case Study on the Brazilian Political Scene. SBBD 2013 pp. 1–6 (2013).
Published
2015-07-20
How to Cite
ALVES, André; BAPTISTA, Cláudio; DE ANDRADE, Luiz Henrique; PAES, Roberta.
Uso de Técnicas de Análise de Sentimentos em Tweets relacionados ao Meio-Ambiente. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 6. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 37-46.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2015.10188.
